Abstract
Population growth and rapid urbanization have resulted in an unprecedented escalated production of Municipal Solid Waste (MSW), prompting extensive and intensive research to seek sound solutions using advanced technology. Machine learning algorithms are specialized in modeling complex nonlinear processes, and have been gradually adopted in recent years to facilitate MSW management and environmental sustainability. A variety of intelligent approaches have been developed for MSW management, ranging from prediction of reliable mapping of nonlinear behaviors between inputs and outputs in physical, chemical and biological processes in models, to emerging optimization and control of algorithms for the classification and management of processes as intelligent systems. This chapter highlights and compares algorithms of intelligent technologies and discusses the state-of-the-art, challenges, outlooks for real-world applications of the intelligent classification and management of MSW. Typical algorithms and their improvements for optimal classification and management of MSW are also presented. Additionally, this chapter has extensively reviewed the applications of intelligent technology in the entire MSW management chain, including generation, collection and transportation, sorting and characterization, disposal and recycling. Ultimately, challenges and opportunities for the application of intelligent technology in the classification and management of MSW are highlighted.
Original language | English |
---|---|
Title of host publication | Solid Waste-Based Materials for Environmental Remediation |
Editors | Guanyu Chen, Ning Lin, Zhanjun Cheng |
Publisher | CRC Press |
Pages | 1-40 |
Number of pages | 40 |
ISBN (Electronic) | 9781040172049 |
ISBN (Print) | 9781032863900 |
DOIs | |
Publication status | Published - 2024 |
Publication type | A3 Book chapter |
Publication forum classification
- Publication forum level 1
ASJC Scopus subject areas
- General Engineering
- General Environmental Science
- General Materials Science